Price: $2,999.00
Length: 3 Days
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Machine Learning for Control Training

Machine Learning for Control Training is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.

Moreover, Machine Learning for Control Training will focus on the intersection of both fields and will describe current and state-of-the-art techniques for implementing machine learning for control applications. Key applications include complex nonlinear systems for which classical linear control theory methods may not be readily applicable.

Machine learning serves to automate the data analysis process by enabling computers and machines to learn from data through experience applied to specific tasks without explicit programming. Control theory serves to control processes and devices (e.g., motors, robots, flight controls, etc.) using sophisticated mathematical techniques and models.

For systems where the mathematical techniques are too computationally complex or are undetermined, machine learning can serve as an input to algorithms in order to control complex dynamical systems.

Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, and techniques including: supervised and unsupervised learning, mathematical and heuristic aspects of data analysis, modeling to describe key algorithms such as linear regression, clustering, classification, and prediction.

Further, attendees will be learning ideas on control theory concepts, including: linear systems control, system identification, open-loop and closed-loop control, non-linear control, system stability considerations, and main control techniques (e.g., adaptive control, intelligent control, optimal control, robust control, etc.).

Additionally, attendees will learn how to adapt machine learning techniques to control applications (e.g., flying a drone, autonomous vehicles, and the like). Machine learning for control provides techniques for computers to learn about big data sets without being programmed explicitly, for example, by using methods of data analysis.

Further machine learning for control applies the information gathered from the big data to control problems of high complexity. Accordingly, such techniques take advantages of data mining approaches, statistics, and other machine learning algorithms to build models for predicting future outcomes for control.

Linear algebra and computer programming are the basis for many of the machine learning for control algorithms. Using machine learning as a tool, the computer must automatically learn the parameters of models from the data. Using larger datasets, better accuracy and performance can be achieved.

Machine learning for control, for example, can be used in proactive maintenance to continuously monitor the performance of simple or complex industrial systems, applications and events. Using the ability to learn and adapt, makes it the optimal choice for improvements in ongoing processes, and to automatically predict and prevent failures.

Learning Objectives

After completing this course, the student will be able to:

  • Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
  • List similarities and differences between AI, Machine Learning and Data Mining
  • Learn how Artificial Intelligence uses data to offer solutions to existing problems
  • Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize
  • Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns
  • Learn the basics of classical control theory
  • Learn the basics of mathematical concepts common to both control theory and machine learning including linear algebra concepts and calculus concepts
  • Learn how to classify the types of learning such as supervised and unsupervised learning
  • Make accurate predictions and analysis to effectively solve potential problems
  • List Machine Learning concepts, principles, algorithms, tools and applications
  • Learn the concepts and operation of various machine learning techniques and algorithms most adaptable to control theory, including, but not limited to, neural networks of various kinds (convolutional, recurrent, long-short term memory, etc.), support vector machines (including non-linear extensions such as kernel support vector machines), probabilistic methods such as naive Bayes and deep belief networks, reinforcement learning
  • Comprehend the theoretical concepts of both machine learning and control theory and how they relate to the practical aspects controlling complex dynamical systems

  COURSE OUTLINE  

The Basics of Machine Learning

  • What is Machine Learning?
  • Emergence and applications of Artificial Intelligence and Machine Learning
  • Basics of Artificial Intelligence
  • Basics of Machine Learning
  • Basics of Data Mining
  • Data Mining versus Machine Learning versus Data Science
  • Data Mining and patterns
  • Why is machine learning important?
  • Creating good machine learning systems

  Popular Machine Learning Methods

  • Supervised learning and unsupervised learning
  • Supervised learning algorithms and labeled data
  • Trained using labeled examples
  • Classification, regression, prediction and gradient boosting
  • Supervised learning and patterns
  • Predicting the values of the label on additional unlabeled data
  • Using historical data to predict likely future events
  • Unsupervised learning and unlabeled data
  • Unsupervised learning against data that has no historical labels
  • Semi supervised learning
  • Using both labeled and unlabeled data for training
  • Classification, regression and prediction
  • Reinforcement learning
  • Robotics, gaming and navigation
  • Discovery through trial and error
  • The agent (the learner or decision maker)
  • The environment (everything the agent interacts with)
  • Actions (what the agent can do)

  Review of Terminology and Principles

  • Math Refresher
  • Concepts of linear algebra
  • Probability and statistics
  • Algorithms
  • Automation and iterative processes
  • Scalability
  • Ensemble modeling
  • Framing
  • Generalization
  • Machine Learning methods
  • Classification
  • Training and Training Set
  • Validation
  • Representation
  • Regularization
  • Logistic Regressions
  • Neutral Nets
  • Neutral Nets
  • Multi class Neutral Nets
  • Embeddings
  • Basic Algebra and Calculus
  • Basic Python
  • Chain rule
  • Concept of a derivative
  • Gradient or slope
  • Linear algebra
  • Logarithms, and logarithmic equations
  • Matrix multiplication
  • Mean, median, outliers and standard deviation
  • Partial derivatives
  • Sigmoid function
  • Statistics
  • Tanh
  • Tensor and tensor rank
  • Trigonometry
  • Variables, coefficients, and functions

  Machine Learning Concepts Related to Control

  • Neural networks
  • Feedforward neural network
  • Backpropagation
  • Cost function
  • Weights, bias, activation function
  • Gradients
  • Vanishing and exploding gradients
  • Stochastic gradient
  • Convolutional neural network
  • Recurrent neural network
  • Long short-term memory (LSTM) networks
  • Fully recurrent neural networks
  • Elman networks and Jordan networks
  • Hopfield network
  • Bidirectional associative memory (BAM) network
  • Gated recurrent unit
  • Reinforcement learning

Introduction to Control

  • State-space representation
  • Open-loop control vs closed loop control
  • State variables
  • Linear systems
  • Linear time-invariant theory,
  • Continuous-time LTI case
  • Step response
  • Impulse response
  • Phase space

  Types of controllers

  • Lead-lag compensator
  • Programmable logic controller
  • Embedded controller

  Frequency-Domain Approach to Control

  • Transfer function
  • Closed loop transfer function
  • Z-transform
  • Laplace transform

  Stability

  • Stability theory
  • Bounded-input bounded-output (BIBO) stability
  • Input-to-state stability (ISS)
  • Bode plot
  • Nichols plot
  • Nyquist plot
  • Routh–Hurwitz stability criterion
  • Root locus analysis: angle condition, magnitude condition
  • Gain margin and phase margin
  • Nyquist stability criterion
  • Root locus method
  • Lyapunov stability

  Machine Learning Control Basics

  • Complex non-linear dynamical systems
  • Classical Approaches: Chaos theory
  • Classical Approaches: Lorenz attractor
  • Nonlinear, multivariable, adaptive and robust control theories
  • Full state feedback (FSF)
  • Pole placement

Machine learning control

  • Intelligent control
  • Neural network control
  • Hidden Markov models (HMMs)
  • Kalman filter
  • Kalman gain
  • Non-linear Kalman filter
  • Extended Kalman filter
  • Unscented Kalman filter
  • Particle filter
  • Reinforcement learning control

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